ADCC

Original Implementation of the paper Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis, appeared at CVPRW'2021 Responsible Computer Vision as oral and poster presentation.

ADCC is a more solid and unbiased benchmark for evaluating CAMs for explainability purposes. It considers more than one aspect of how is the resulting saliency map (read the paper for a more detailed overview)

How it works

Given:

1. An input image
2. A saliency map
3. An explanation map
4. A CNN
5. A saliency map extractor (a callable returning an upsampled saliency map)

it computes the Average Drop, the Coherency and the Complexity, to return the final ADCC score.

Steps

Run the example

Using default parameters

main.py

Using custom parameters

main.py --image [path-to-input-image,str] --model [name-of-the-CNN,str]

Use it as a module

The ADCC module provides all the computation needed to return the final score, given the 5 inputs previously mentioned.

What it returns

The ADCC module simply returns the ADCC score in [0,1] range

Other infos

This repo is implemented in PyTorch

If you use this repo in your publication please cite

@inproceedings{poppi2021revisiting,
  title={Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis},
  author={Poppi, Samuele and Cornia, Marcella and Baraldi, Lorenzo and Cucchiara, Rita},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year={2021}
}

For any info contact samuele.poppi@unimore.it